Bivariate_practice

################################BIVARIATE INFERENTIAL STATISTICS##############################################################

####ANOVA for a quantitative dependent variable (DV) and categorical independent variable (IV)
#run an analysis of variance (ANOVA); only change variable names and put in this order DV ~ IV
#IMPORTANT: USE THE COPY OF YOUR CATEGORICAL VARIABLE THAT YOU USED FOR THE BAR GRAPH (variable name ends in cat)
data.aov1 <- aov(wave5addhealth$H5ID23 ~ wave5addhealth$H5HR2cat, data=wave5addhealth)
summary(data.aov1)
#remove hashtag on line below to run Tukey ONLY if the F test is statistically significant AND there are more than 2 categories on the IV.
#TukeyHSD(data.aov1)

##INTERPRETATION OF ANOVA: The ANOVA results show a statistically signficant relationship between living arrangements
# and the amount of time spent watching TV, movies, and videos among adults in the United States (F=8.58; p<.05).
# Tukey's post-hoc test shows more specifically that adults who live in their parents' home or another persons' home
# watch significantly more TV, movies, or videos than those who live in their own place (p<.05). There is a difference
# of about 4 hours per week between those living arrangements.


####CHI SQUARE for categorical independent variable and categorical dependent variable
#only change the variable names
data.chisq1 <- chisq.test(wave5addhealth$H5OD2A, wave5addhealth$H5HR2)
data.chisq1

##INTERPRETATION OF CHI SQUARE: The chi square test of independence shows that there is a statistically significant
# association between sex at birth and living arrangements among adults in the U.S. (chi squared=14.715; p<.05).


####CORRELATION for 2 quantitative variables
#only change the variable names
cor.test(wave5addhealth$Age, wave5addhealth$H5ID23)

##INTERPRETATION OF CORRELATION: There is not a statistically significant association between age and amount of
# time that adults in the U.S. spend watching TV, movies, or videos (Pearson's correlation= .013; p>.05).